This thesis investigates the economic interrelationships that tourism has in the wider economy in the context of a country that is heavily reliant on tourism revenues. More specifically, it seeks to examine the welfare, intersectoral, distributional, competitive, investment and dynamic issues relating to the tourism sector that have been under investigated in both the tourism and trade literature. These issues have been investigated empirically using Computable General Equilibrium (CGE) analysis. The thesis is set out as follows: Chapter 1 sets out the relative position of Spain in terms of its international competitors and defines the tourism sector. It also explains why CGE modelling is felt to be the most suitable approach for modelling the Spanish tourism sector for the purposes of this thesis. It also presents an overview of the planned research. Chapter 2 gives an overview of the structure and key features of the Spanish economy. It discusses the evolution of the tourism sector and how it varies between the different autonomous communities in Spain. The Spanish Tourism Satellite Account is presented and Spanish tourism policy is examined. Chapter 3 reviews the theoretical and empirical literature on CGE modelling and tourism analysis relevant to this thesis. Various types of CGE model are scrutinised and their usefulness assessed. The role of tourism in international trade is considered and the characteristics of the tourism sector that need to be embodied into a CGE model are discussed. Chapter 4 describes the core CGE model used in this thesis and the underlying equations that are associated with it. The central data set used is the Spanish input-output table for 1996. This data set is described and all subsequent input-output tables used in other chapters are amended so as to be consistent with this data set. Closure rules, elasticity parameters, solution methods and calibration methods are also discussed. Chapter 5 presents the results of the experiments carried out with the dynamic Spanish national CGE model. The core model presented in Chapter 4 has been extended to incorporate foreign direct investment and these changes are disclosed in the opening sections. Counterfactuals are designed so as to estimate the impact of foreign direct investment inflows and tourism demand shocks on the Spanish economy. Sensitivity analysis of the key exogenous parameters is also undertaken. Chapter 6 presents the results of the experiments carried out on the static regional CGE model of the regions of Spain. Input-Output tables for four of Spain's autonomous regions were obtained and integrated with the Spanish national table to create a data set which accounts for the four regions analysed and the remainder of the Spanish economy. The model presented in Chapter 4 is adapted to incorporate regional trade flows and structural differences are discussed. Counterfactuals are designed in order to investigate how regional tax policy might affect tourism flows in Spain and how tourism demand impacts on different regions in Spain. Sensitivity analysis of the key exogenous parameters is also undertaken. Chapter 7 presents the results of the experiments of the dynamic CGE model for the Canary Islands. The core model is identical to that presented in Chapter 4, except that it is applied at a sub-national rather than a national level. Counterfactuals are designed so as to take account of the issues affecting a small island economy that is heavily reliant on tourism. As before, sensitivity analysis of the key exogenous parameters is also undertaken. Chapter 8 summarises the findings of this study, highlights possibly policy implications and cites limitations of the research. Suggestions for further research are also highlighted.

Within last 20 years, a number of methods have been employed for classifying remote sensing data, including parametric methods (e.g. the maximum likelihood classifier) and non-parametric classifiers (such as neural network classifiers).Each of these classification algorithms has some specific problems which limits its use. This research studies some alternative classification methods for land cover classification and compares their performance with the well established classification methods. The areas selected for this study are located near Littleport (Ely), in East Anglia, UK and in La Mancha region of Spain. Images in the optical bands of the Landsat ETM+ for year 2000 and InSAR data from May to September of 1996 for UK area, DAIS hyperspectral data and Landsat ETM+ for year 2000 for Spain area are used for this study. In addition, field data for the year 1996 were collected from farmers and for year 2000 were collected by field visits to both areas in the UK and Spain to generate the ground reference data set. The research was carried out in three main stages.The overall aim of this study is to assess the relative performance of four approaches to classification in remote sensing - the maximum likelihood, artificial neural net, decision tree and support vector machine methods and to examine factors which affect their performance in term of overall classification accuracy. Firstly, this research studies the behaviour of decision tree and support vector machine classifiers for land cover classification using ETM+ (UK) data. This stage discusses some factors affecting classification accuracy of a decision tree classifier, and also compares the performance of the decision tree with that of the maximum likelihood and neural network classifiers. The use of SVM requires the user to set the values of some parameters, such as type of kernel, kernel parameters, and multi-class methods as these parameters can significantly affect the accuracy of the resulting classification. This stage involves studying the effects of varying the various user defined parameters and noting their effect on classification accuracy. It is concluded that SVM perform far better than decision tree, maximum likelihood and neural network classifiers for this type of study. The second stage involves applying the decision tree, maximum likelihood and neural network classifiers to InSAR coherence and intensity data and evaluating the utility of this type of data for land cover classification studies. Finally, the last stage involves studying the response of SVMs, decision trees, maximum likelihood and neural classifier to different training data sizes, number of features, sampling plan, and the scale of the data used. The conclusion from the experiments presented in this stage is that the SVMs are unaffected by the Hughes phenomenon, and perform far better than the other classifiers in all cases. The performance of decision tree classifier based feature selection is found to be quite good in comparison with MNF transform. This study indicates that good classification performance depends on various parameters such as data type, scale of data, training sample size and type of classification method employed.

The majority of empirical studies of tourism analysis use a static single equation approach to model the demand for tourism of one origin for one or more destination countries. The examination of such studies generally shows that the economic interpretation and policy implications drawn as conclusions are based on mis-specified models, invalid estimation and inference procedures, inconsistent estimates and poor forecasting performance. Static single equation models of tourism demand tend to neglect interdependencies among destinations, ignore nonstationarity, overlook dynamics and, generally, disregard economic theory. Empirical specifications constrained by these flaws are bound to generate biased and inconsistent estimates upon which no reliable economic analysis or policy implication can be based. In an analytical context that focuses on the UK tourism demand for France, Spain and Portugal in the period 1969-1997, the main objective of this thesis is to demonstrate that consistent elasticities' estimates and reliable forecasts can be obtained from empirical models which are based on the principles of economic theory, and specified and rigorously tested within the rules of sound econometric methodology. The alternative models estimated in chapters 4 to 7 include error-correction autoregressive distributed lag models (ARDL), static and dynamic almost ideal demands systems (AIDS) and cointegrated vector autoregressive models (VAR). The main findings that emerge from the study are as follow. The battery of diagnostic tests applied to the dynamic error-correction ARDL models provide sufficient evidence to classify them as statistically robust, structurally stable and well-defined specifications. The evidence obtained for the AIDS and VAR systems indicates them as data-coherent and theoretically-consistent models, complying with the utility maximisation hypotheses. The similarity, across models, of the estimates of the long-run structural parameters and the accuracy of the forecasts they provide further support the reliability of these models for explaining and predicting the UK tourism demand behaviour, in contrast to the static single equations estimated in chapter 3. The specifications of chapters 4 to 7 can easily be extended, without loss of generality, to more origins and destinations and can be adapted to alternative contexts such as the demand for specific regions within a country, specific resorts within a region or even specific types of tourism products such as accommodation or leisure facilities, within a local area.

The concept of autonomous pedestrian navigation is often adopted for indoor pedestrian navigation. For outdoors, a Global Positioning System (GPS) is often used for navigation by utilizing GPS signals for position computation but indoors, its signals are often unavailable. Therefore, autonomous pedestrian navigation for indoors can be realized with the use of independent sensors, such as low-cost inertial sensors, and these sensors are often known as Inertial Measurement Unit (IMU) where they do not rely on the reception of external information such as GPS signals. Using these sensors, a relative positioning concept from initialized position and attitude is used for navigation. The sensors sense the change in velocity and after integration, it is added to the previous position to obtain the current position. Such low-cost systems, however, are prone to errors that can result in a large position drift. This problem can be minimized by mounting the sensors on the pedestrian’s foot. During walking, the foot is briefly stationary while it is on the ground, sometimes called the zero-velocity period. If a non-zero velocity is then measured by the inertial sensors during this period, it is considered as an error and thus can be corrected. These repeated corrections to the inertial sensor’s velocity measurements can, therefore, be used to control the error growth and minimize the position drift. Nonetheless, it is still inadequate, mainly due to the remaining errors on the inertial sensor’s heading when the velocity corrections are used alone. Apart from the initialization issue, therefore, the heading drift problem still remains in such low-cost systems. In this research, two novel methods are developed and investigated to mitigate the heading drift problem when used with the velocity updates. The first method is termed Cardinal Heading Aided Inertial Navigation (CHAIN), where an algorithm is developed to use building ‘heading’ to aid the heading measurement in the Kalman Filter. The second method is termed the Rotated IMU (RIMU), where the foot-mounted inertial sensor is rotated about a single axis to increase the observability of the sensor’s heading. For the CHAIN, the method proposed has been investigated with real field trials using the low-cost Microstrain 3DM-GX3-25 inertial sensor. It shows a clear improvement in mitigating the heading drift error. It offers significant improvement in navigation accuracy for a long period, allowing autonomous pedestrian navigation for as long as 40 minutes with below 5 meters position error between start and end position. It does not require any extra heading sensors, such as a magnetometer or visual sensors such as a camera nor an extensive position or map database, and thus offers a cost-effective solution. Furthermore, its simplicity makes it feasible for it to be implemented in real-time, as very little computing capability is needed. For the RIMU, the method was tested with Nottingham Geospatial Institute (NGI) inertial data simulation software. Field trials were also undertaken using the same low-cost inertial sensor, mounted on a rotated platform prototype. This method improves the observability of the inertial sensor’s errors, resulting also in a decrease in the heading drift error at the expense of requiring extra components.

A complete view of cultural tourism requires perspectives on both its economic aspect and its cultural dimension. This thesis presents the first cultural tourist taxonomy in the literature, which classifies the various types of cultural tourists by using fundamental distinctions based on economic theory. It also explains the necessity of classifying cultural tourists into those six well-defined categories, and why it should only be six. Building on McKercher and du Cros (2002), it models the causes and measures the consequences of cultural tourism, and develops a framework for evaluating the economic and cultural impacts caused by cultural tourist attractions. The method of evaluating the economic impact of cultural tourist attractions is based on the causal chain model, and it has improved the approach used in Femandez-Young and Young (2008) and Young et al (2010), which attributes to an attraction the amount of tourist expenditure at the destination caused by the existence of the attraction. The method of measuring the cultural impact is a new contribution to the literature, as this study provides a way to quantify the complex concept of cultural impact, using the ideas of meta-preferences and preference formation (Sen, 1977; 1983; 2002). This research has succeeded in developing a theoretically-based and practically applicable method for measuring and combining the economic and cultural impacts of cultural attractions. The methods have been applied to two cultural attractions in Nottingham: Nottingham Contemporary and the Galleries of Justice. The collected empirical results have demonstrated the feasibility and practicability of the evaluation method based on the new taxonomy. The combined evaluation method enables policy-makers to evaluate comprehensively the overall impact of each attraction and locate the attraction in the cultural space by taking both economic and cultural impacts into account.

This thesis considers how to improve levels of understanding of different stakeholders' perspectives and their involvement in sustainable tourism development. Jeju Island has been relying on tourism to support the economy and has consequently emerged as the most developed tourist destination in Korea, launched as a result of growth-oriented regional policies of the central and local government over the last thirty years. For sustainable tourism to be successful, it requires the stakeholders' support in the community to develop tourism in a sustainable manner. Therefore, this study focuses on building knowledge about stakeholder perceptions of government led tourism development by investigating stakeholder groups. Also, for this research aim, a qualitative approach was applied, interviews are used to take information for a specific purpose, and this research used semi-structured interviews to obtain relevant information from 42 key informants. For analysis of the qualitative data from the key informants, this study employed Grounded Theory (GT) as a tool for data analysis and interpretation. This research is offers a critical evaluation towards the perceptions and impacts of tourism development and involvement, and investigates their relative influence within the collaboration process. To achieve the collaboration between various stakeholders in supporting the goals and objectives of tourism development, the study presents that the local government should involve local residents more actively in the decision making processes of the tourism development. The study confirms the importance of trust as a key variable in a social exchange relationship between residents' of a host community and government actors and all stakeholders need to be educated and trained to make sustainable tourism development more feasible.

Conserving the cultural significance of heritage sites - as the guardians of social unity, place identity, and national pride - plays an essential role in maintaining sustainable social development, as well as preserving the variations identifying cultural groups and enriching the interaction between them. Consequently, and considering the importance of the built environment in communicating, as well as documenting, cultural messages, this research project, started in 2007, develops a set of guiding principles for interpretation management, as a process for conserving cultural World Heritage Sites; by maintaining and communicating their cultural significance through managing newly added architectural, urban, and landscape designs to such heritage sites. This research was mainly conducted to investigate and explain a concern regarding a gap that is increasing between people and the cultural heritage contexts they reside- particularly in Egypt- and to suggest a strategy for professionals to understand such sites from a perspective that reflects the public cognition. Adopting Grounded Theory methodology, the research develops a series of principles, which are intended to guide the process of cultural heritage conservation; through a critical analysis of current heritage conservation practices in World Heritage Sites. The research shows how they [the guiding principles] correspond to the contemporary perception of cultural heritage in literature, for which, a thorough discussion of literature, as well as critical analysis of UNESCO’s heritage conventions and ICOMOS charters are carried out. The research raises, discusses, and answers several key questions concerning heritage conservation, such as: whether UNESCO’s conventions target the right heritage or not; the conflicts appearing between heritage conservation documents (conventions and charters); whether intangible heritage can be communicated through design; and the effect of Western heritage ideology on heritage conservation practices. This is carried out through the use of interpretive discourse analysis of literature and heritage documents, and personal site observations and questionnaire surveys carried out in two main World Heritage Sites: Historic Cairo in Egypt and Liverpool city in the UK. The two case studies contributed to the understanding of the general public’s perception of cultural Heritage Sites, and how such perception is reflected in current heritage conservation practices. The thesis decodes cultural World Heritage Sites into three intersecting levels: the ‘cultural significances’ (or ‘open codes’), which represent different categories under which people perceive historic urban landscapes; the ‘cultural concepts’ (or ‘axial codes’), which are considered as the objectives of heritage conservation practice, and represent the general concepts under which cultural significances influence the heritage interpretation process; and finally, the ‘interpretation strategy tactics’, the UNCAP Strategy (or the ‘selective coding’), which are the five overarching principles guiding the interpretation management process in cultural heritage sites. This strategy, the UNCAP (Understanding people; Narrating the story; Conserving the spirit of place; Architectural engagement; and Preserving the built heritage), developed throughout this research, is intended to help heritage site managers, curators, architects, urban designers, landscape architects, developers, and decision makers to build up a thorough understanding of heritage sites, which should facilitate the establishment of more interpretive management plans for such sites, and enhance the communication of meanings and values of their physical remains, as well as emphasizing the ‘spirit of place’; for achieving socio-cultural sustainability in the development of World Heritage Sites.

This thesis addresses the non-linear ill-posed inverse problems of reconstructing the three-dimensional distribution of electrical conductivity in Earth's mantle. This problem has never previously been fully attacked. The major objective of this thesis is to develop a methodology allowing to resolve any large-scale three-dimensional inhomogeneities in Earth's mantle based on a regularised inversion of electromagnetic field data. We generalise the global three-dimensional forward problem of electromagnetic induction in the frequency domain for arbitrary sources, and solve it in a linear algebraic formulation. We develop the data sensitivities analysis based on the generalised forward problem, and derive the analytic and numerical expressions for the Jacobian and the derivative of the regularised least squares penalty functional. This allows us to set up a non-linear conjugate gradient inverse solution. In doing so, we parameterize the model space by layered spherical harmonics. This inverse solution is tested on a series of checkerboard experiments; on this basis, we discuss spatial resolution of our analysis at different depths in the mantel. This methodology is then applied to the most current low-frequency global observatory data set, and models are obtained satisfying the data statistically well. We discuss the features of these models and the implications of our experiments. We also plot and compare the corresponding magnetic fields and responses at the Earth's surface, and provide suggestions for future directions of research.

Since 2000, a new round of suburbanisation characterised by mixed-use clustered development has begun to unfold in China. This research aims to explore the dynamics of recent suburban growth in China and also provide an empirical case for enriching suburban theory. It is held that suburbanisation in China in its current form is by no means a spontaneous process, but results from capitalism’s creation of a new space to facilitate accumulation. Based on this view, the study examines the underlying forces of contemporary suburban growth with regard to three questions: what is the role of suburbanisation in China’s contemporary capital accumulation regime? How are the suburbs developed under coalitions of different actors? And how is suburban development shaped by demand-side actors? The study is founded on an intensive case study of Shanghai and one of its suburban districts, Songjiang. Both qualitative and quantitative research methods are used. Firsthand data from interviews and a questionnaire survey and a wide variety of secondary data were collected, providing a rich fund of knowledge for the research. While similar forms and functions to (post)-suburban settlements that have recently emerged in Western countries are found in Chinese suburbs, suburbanisation through new town development in China is a strategy of capital accumulation in response to a range of new conditions specific to China’s local context. New towns deal with the recentralisation of both fiscal and land development powers on the one hand, and accommodate the increasing housing demands of a diverse labour force on the other. Moreover, a peculiar land-centred accumulation regime is established in which real estate development and industrial development mutually reinforce each other. The development of these suburban nodes is organised under state-led entrepreneurial governance in which coalitions are led by the suburban district government and followed by various public and private sectors; however, this is manipulated by the municipal government. Demand-based driving forces are manifold, including four interwoven processes: industrial restructuring and economic growth, outwards-migration, urbanisation, and capitalisation. Three types of people, namely migrants, local natives and residents from central Shanghai, constitute the main groups of suburban residents. Their spatial sorting creates a heterogeneous suburban space.

Retrospective urban air quality assessments are imperative in understanding human health effects of long-term (lifetime) exposures to pollution; however, historical pollution data are limited. Therefore, air pollution records captured in the sediments of small urban ponds, sedimentary archives sensitive to localised urban activity, have been explored within the lower Mersey Region (LMR), a heavily industrialised area with poor health amongst the population. Proxy pollution profiles derived from isotope chronologies and magnetic, trace metal, flyash and geochemical properties of sediments from four small urban ponds have yielded high-resolution air pollution histories (<300 years). These site specific air pollution signals reveal air quality impacts of localised urban development and demonstrate the integrity of sediment records from urban ponds. A cross-regional air pollution history, reconstructed using these local contamination signals, details spatial variations in pollution deposition across the LMR, spanning pre-industrial times to present day. Low -pre-1830 pollution levels reflect a time of pre-intensified industry in the LMR with post-1830 increases signifying the Industrial Revolution and establishment of the early chemical industry. Distinct pollution enhancement from -1900 to 1950 is attributed to population increases, industrial diversification and war-time demands on industry, pre-Clean Air Acts (1959). A complex cross-regional post-1950 signal reveals intra-urban variations reflecting urban expansions within the region, with pollution reductions observed post-1970, due to increasingly stringent air quality legislations, however, pollution does not consistently decline throughout the LMR, potentially, due to air quality impacts from increased road and air travel. This high-resolution cross-regional urban air pollution signal provides an alternative to reliance upon background air pollution signals recorded by remote UK lakes. Furthermore, these ponds are set amongst populations most as risk to exposure of urban PM, and demonstrate important spatial variations in pollution characteristics, as well as revealing how air pollution has changed over time. These air pollution histories may, therefore, be used to better understand possible health linkages in the LMR.